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3rd IEEE World Conference on applied Intelligence and Computing, AIC 2024

作者： Singh, Jagendra Sahu, Dinesh Prasad Kumar, Ashwini Ahmad, Salman Khursheed Majumder, Jayeeta Gangopadhyay, Sourav Bennett University School of Computer Science Engineering & Technology Greater Noida India Camellia Institute of Technology & Management Department of Computer Science & Engineering Jaipur India Gl Bajaj Institute of Technology & Management Dept. of Applied Computational Science and Engineering Greater Noida India Brainware University Department of Hospital Management Kolkata India

ISBN: (纸本)9798350384598

This study investigates the optimization of healthcare accessibility and effectiveness by integrating artificial intelligence (AI) solutions within Internet of Things (IoT) systems for remote patient monitoring. Embracing a holistic approach, we deploy medical sensors, including temperature, pressure, heart rate, and oxygen sensors, to collect patients' continuous health metrics (vital signs). These sensed data are then transmitted to an Arduino controller, responsible for managing data communication with the cloud infrastructure using a Wi-Fi module that facilitates data storage and analysis. Stored data are accessed by authorized healthcare professionals, who exploit machine learning models, including Artificial Neural Networks (ANN), Logistic Regression (LR) and Decision Tree (DT), to forecast patients' health outcome in advance using the sensed data. A rigorous evaluation demonstrated the IoT system's effectiveness, based on testing with sensed data from ten patients over a month, where 70% of the data were used for training the models, and the rest for testing. The proposed IoT system demonstrates the success;ANN exhibited the highest accuracy at 97.88%, followed by LR at 93.43%, and DT at 91.20%. Precision, recall, and F1 scores further validated the models' ability to predict health outcome accurately. Evaluation of the accuracy and loss curves showed the convergence of the models towards optimal performance. This study contributes to remote healthcare monitoring and provides a promising IoT paradigm for realizing ubiquitous remote monitoring, optimized healthcare effectiveness, and patient outcome through AI-enabled IoT systems. © 2024 IEEE.

关键词： Remote patient monitoring

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Exploring household food security through institutional factors: A statistical and mathematical analysis

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9179-9195页

作者： Khan, Younas Ashraf, Shahzaib Farman, Muhammad Abdallah, Suhad Ali Osman Department of Sociology Kohat University of Science and Technology Kohat Pakistan Institute of Mathematics Khawaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Department of Mathematics Faculty of Arts and Science Near East University Turkey Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Applied College Khamis Mushait King Khalid University Abha Saudi Arabia

Achieving household food security is the tumbling issue of the century. This article explores the factors affecting household food security and solutions by utilizing a synergy of statistical and mathematical models. The methodology section is divided into two portions namely sociological and mathematical methods. Sociologically, 379 household heads were interviewed through structured questions and further analyzed in terms of descriptive and binary logistic regression. The study found that 4 independent variables (poverty, poor governance, militancy, and social stratification) showed a significant association (P = 0.000) to explain variations in the dependent variable (household FS). The Omnibus test value (χ2= 102.386;P = 0.000) demonstrated that the test for the entire model against constant was statistically significant. Therefore, the set of predictor variables could better distinguish the variation in household FS. The Nagelkerke's R Square (R2 = .333) helps to interpret that the prediction variable and the group variables had a strong relationship. Moreover, 23% to 33% variation in FS was explained by the grouping variables (Cox and Snell R2 = 0.237 and Nagelkerke's R2 = 0.333). The significant value of Wald test results for each variable confirmed that the grouping variables (poor governance P = 0.004, militancy P = 0.000, social stratification P = 0.021 and poverty P = 0.000) significantly predicted FS at the household level. mathematically, all the statistics were validated further through the application of spherical fuzzy mathematics (TOPIS and MADM) to explore what factors are affecting household FS. Thus, the study found that F3 (poverty) > F2 (militancy) > F4 (social stratification) > F1 (poor governance) respectively. Thus, it could be concluded from these findings that the prevalence of poverty dysfunctional all the channels of household FS at the macro and micro levels. Therefore, a sound and workable model to eradicate poverty in the study area by

关键词： Food supply

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Swallowing Assessment using High-Resolution Cervical Auscultations and Transformer-based Neural Networks 46

Swallowing Assessment using High-Resolution Cervical Auscult...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Anwar, Ayman Khalifa, Yassin Lucatorto, Erin Coyle, James L. Sejdic, Ervin University of Toronto Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada University of Pittsburgh Center for Research Computing and Information Technology Analytics PittsburghPA United States Cairo University Faculty of Engineering Systems and Biomedical Engineering Department Giza Egypt University of Pittsburgh School of Health and Rehabilitation Sciences Department of Communication Science and Disorders PittsburghPA United States University of Toronto North York General Hospital Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada

ISBN: (纸本)9798350371499

Swallowing assessment is a crucial task to reveal swallowing abnormalities. There are multiple modalities to analyze swallowing kinematics, such as videofluoroscopic swallow studies (VFSS), which is the gold standard method, and high-resolution cervical auscultation (HRCA), which is a noninvasive technique that uses a triaxial accelerometer attached to the patient's neck. Deep learning models play an essential role in data driven analysis of swallowing landmarks using VFSS and/or HRCA as input data. Most of these models utilize convolutional and recurrent neural networks. Here, we investigate the ability of transformers to analyze swallowing kinematics;specifically upper esophageal sphincter opening and laryngeal vestibule closure using HRCA signals. We tested the model using an independent test dataset to assess the generalizability of the proposed network. The proposed network achieved an average detection accuracy higher than 90% and 85% for both segmentation tasks, which outperform the hybrid neural networks from the literature, and the model obtained high-performance measures for the independent dataset, showing the transformers' ability to generalize on unseen data. © 2024 IEEE.

关键词： Recurrent neural networks

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Enhancing Fake News Detection Using BERT: A Comparative Analysis of Logistic Regression, RFC, LSTM and BERT

Enhancing Fake News Detection Using BERT: A Comparative Anal...

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Ramzan, Alishba Ali, Raja Hashim Ali, Nisar Khan, Ayesha Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany Faculty of Engineering and Applied Science University of Regina ReginaS4S0A2 Canada

ISBN: (纸本)9798331504465

The research analyzed how to identify fake news effectively, understanding the developing capabilities to differentiate between misinformation and the correct nature of news in today’s complex media environment. Numerous approaches were tested: mostly the ways of determining if a certain message is valid or false. With application of Logistic Regression, Random Forest and lastly LSTM models, the research has been able to achieve 98.7%, 98.8%, and 95% accuracies respectively, showing that these traditional and advanced techniques are very effective. However, it became apparent that these models performed poorly as soon as tested on a new dataset and the discrepancy was not being able to understand context while being trained. Understanding the context is the key in news identification, so the research turned to BERT, the Transformer model pre-trained with huge contextual data to perform this task. Drawing on its deep and diverse knowledge base, BERT has shown a remarkable aptitude for sorting news articles into fake/real categories based on their context. Logistic Regression, Random Forest, and LSTM models demonstrated that although they were able to build models that were highly accurate up to 99% on familiar data, their accuracy dropped disproportionately as soon as new data was given. BERT, despite having a lower overall accuracy of 84% demonstrated a better sensitivity to contextual nuances in the news data. Here, the importance of contextualized conception in the sphere of fake news detection should be emphasized as a way to take advantage of BERT’s complicated comprehension which is a promising alternative of more accurate and effective identification as the media landscapes of the modern times may be complex and diverse. Despite the fact that the classical models surpass in planned environments, BERT’s capability of contextualization made it irreplaceable for evidence as the news sources in uncommon circumstances can digress from base. © 2024 IEEE.

关键词： Logistic regression

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DISTANCE BETWEEN SPECIES BY CONFUSION OPERATORS OF MULTI-CLASS CLASSIFIERS

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UPB Scientific Bulletin, Series A: applied mathematics and Physics 2025年第1期2025卷 171-182页

作者： Tarbă, Nicolae Irimescu, Ionela N. Pleavă, Ana M. Scarlat, Eugen N. Mihăilescu, Mona Doctoral School Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology POLITEHNICA Bucharest Romania Applied Sciences Doctoral School National University of Science and Technology POLITEHNICA Bucharest Romania CAMPUS Research Center National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept Research Center for Applied Sciences in Engineering National University of Science and Technology POLITEHNICA Bucharest Romania

We introduce a method to evaluate the similarities between classes of objects based on the confusion matrices coming from the multi-class machine learning (ML) predictors that operate in the vector space generated by the classes. Operation in the confusion domain allows us to use the distance as evaluation metric of the closeness of the relevant properties carried by the classes. We applied the method to semiquantitatively evaluate the effects of X-ray and carbon ion radiation on cultured SW1353 chondrosarcoma cells with respect to the non-irradiated reference. We prove that it is possible to draw useful information on the strength of cells nuclei changes. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Radiation effects

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Energy-Efficient Network Slicing for UAV-enabled Networks 10

Energy-Efficient Network Slicing for UAV-enabled Networks

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Al-Habob, Ahmed A. Dobre, Octavia A. Jing, Yindi Memorial University Faculty of Engineering and Applied Science St. John'sNL Canada University of Alberta Department of Electrical and Computer Engineering EdmontonAB Canada

ISBN: (纸本)9798350373011

This paper investigates an unmanned aerial vehicle (UAV)-enabled network slicing problem to provide content delivery and sensing data gathering services. Two tenants provide services to their clients by sharing a common infrastructure of a set of UAVs. The content delivery tenant needs to guarantee that each of its clients (users) receives the required content, while the sensing tenant aims to gather an adequate amount of uncorrelated data. An energy consumption minimization framework is considered to meet the tenants' requirements by optimizing the number of deployed UAVs, the deployment location of each UAV, the transmit power of each deployed UAV, and the user-UAV association. Taking into consideration the spatial correlation among the sensing users, a subset of these users is activated to gather the required sensing information. A heuristic solution approach is presented to select the suitable deployment location of the UAVs, activate the optimum subset of the sensing users, and associate the users of both tenants with the deployed UAVs. Simulation results illustrate that the proposed framework significantly reduces the total energy consumption. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection

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computer Systems science & engineering 2023年第9期46卷 3103-3119页

作者： Khaled M.Alalayah Fatma S.Alrayes Mohamed K.Nour Khadija M.Alaidarous Ibrahim M.Alwayle Heba Mohsen Ibrahim Abdulrab Ahmed Mesfer Al Duhayyim Department of Computer Science College of Science and ArtsNajran UniversitySharurahSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Malware is a‘malicious software program that performs multiple cyberattacks on the Internet,involving fraud,scams,nation-state cyberwar,and *** malicious software programs come under different classifications,namely Trojans,viruses,spyware,worms,ransomware,Rootkit,botnet malware,*** is a kind of malware that holds the victim’s data hostage by encrypting the information on the user’s computer to make it inaccessible to users and only decrypting it;then,the user pays a ransom procedure of a sum of *** prevent detection,various forms of ransomware utilize more than one mechanism in their attack flow in conjunction with Machine Learning(ML)*** study focuses on designing a Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection(LBAAA-OMLMD)approach in computer *** presented LBAAA-OMLMDmodelmainly aims to detect and classify the existence of ransomware and goodware in the *** accomplish this,the LBAAA-OMLMD model initially derives a Learning-Based Artificial Algae Algorithm based Feature Selection(LBAAA-FS)model to reduce the curse of dimensionality ***,the Flower Pollination Algorithm(FPA)with Echo State Network(ESN)Classification model is *** FPA model helps to appropriately adjust the parameters related to the ESN model to accomplish enhanced classifier *** experimental validation of the LBAAA-OMLMD model is tested using a benchmark dataset,and the outcomes are inspected in distinct *** comprehensive comparative examination demonstrated the betterment of the LBAAAOMLMD model over recent algorithms.

关键词： computer networks machine learning security malware detection feature selection ransomware

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Traffic Sign Recognition System

Traffic Sign Recognition System

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Ahmed, Maqsood Ali, Raja Hashim Ali, Nisar Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany Faculty of Engineering and Applied Science University of Regina ReginaS4S0A2 Canada

ISBN: (纸本)9798331504465

Traffic Sign Recognition Systems (TSRS) are instrumental in improving road safety by assisting drivers and supporting autonomous vehicles in real-time identification of road regulations. These systems have advanced significantly in recent years, with deep learning approaches achieving promising results. However, despite these developments, existing models face challenges in adapting to the diverse conditions of real-world environments, including variations in lighting, weather, and sign appearance. Addressing this limitation, our study focuses on enhancing the robustness and accuracy of TSRS for practical deployment. In response to this gap, we introduce a novel deep learning model optimized for traffic sign recognition across various conditions. Our approach utilizes a convolutional neural network (CNN) architecture, which was trained on the German Traffic Sign Recognition (GTSR) dataset. We improved the model’s adaptability to new and unseen data while achieving a high accuracy rate of 97.99% by applying techniques like data augmentation and transfer learning. Methodologically, the model workflow includes extensive preprocessing, hyperparameter tuning, and real-time inference evaluations, ensuring suitability for deployment in autonomous driving systems. Our study contributes by providing a scalable, high-accuracy TSRS model that can reliably identify traffic signs in complex environments, supporting both autonomous and assisted driving technologies. The model’s performance paves the way for safer and more efficient road transport, while our results highlight the potential for further interdisciplinary research to expand TSRS capabilities. © 2024 IEEE.

关键词： Convolutional neural networks

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IoT-Enabled Health Monitoring System for Safeguarding Vital Organs with Cloud-Based Diagnosis and Advanced Algorithms 4

IoT-Enabled Health Monitoring System for Safeguarding Vital ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Ahmad, Salman Khursheed Ikra, K.M. Sharma, Preeti Kaushik, Yogita Choudhary, Amar Tripathi, Pradeep Kumar Gl Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Mangalmay Institute of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Raj Kumar Goel Institute of Technology Department of Computer Science -DS Ghaziabad India Sunder Deep Group of Institutions Department Mca Ghaziabad India Alliance University Department of Electronics and Communication Engineering Bengaluru India Ajay Kumar Garg Engineering College Department of Information Technology Ghaziabad India

ISBN: (纸本)9798350360165

This study presents a novel IoT-enabled fitness tracking device intended to improve patient care through the capture of real-time physiological records and predictive analytics. The system uses a variety of sensors, including an Arduino UNO, an LM35, a MAX30100 pulse charge and SPo2 sensor, and a stress sensor, to continuously gather health data from ten different patients. Proactive health assessments and early intervention are made possible by the combination of cloud-based analysis and advanced system learning models, such as Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbours (KNN), and Recurrent Neural Networks (RNN). Tested on real-world datasets, the experimental results show how effective the device is at making accurate and timely predictions. Notably, SVM is the most accurate variant, closely followed by ANN, KNN, and RNN. The confusion matrices ensure accurate and thorough examination of each version's performance, aiding in selecting the most relevant set of rules for real-time health monitoring to be used by clinicians. The present study, as groundbreaking work, demonstrates a major leap in merging IoT with device expertise in the healthcare sector that usages new possibilities in proactive and personalized treatment strategies. The recommended tool's innovative and reliable predictive analytics can potentially revolutionize patient transportation in the healthcare system. © 2024 IEEE.

关键词： Electronic health record

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Predictive Beamforming Approach for Secure Integrated Sensing and Communication

Predictive Beamforming Approach for Secure Integrated Sensin...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

ISBN: (纸本)9798350351255

This paper considers an integrated sensing and communication (ISAC) system framework, in which an aerial eavesdropper poses the threat to intercept the downlink communication from a base station to a set of users. The eavesdropper is moving and its unknown location is estimated based on the echo signal. A maximum likelihood-based scheme is developed to estimate the eavesdropper channel, which performs a coarse estimation and further refines the estimated parameters. A long short-term memory deep network is employed to predict the eavesdropper channel and also to enable a less frequent estimation process. Based on the predicted eavesdropper's channel, a precoding optimization algorithm is developed to improve the sum secrecy rate for the users. Simulation results illustrate that the developed framework provides substantial improvement in the communication secrecy when compared with other benchmark approaches. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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